Ingeniería de software y computación
2023-08-01
Doctor en Ciencias de la Electrónica. Magíster en Ingeniería Electrónica y Telecomunicaciones Ingeniero en Electrónica y Telecomunicaciones
Biomecánica, Dispositivos para el análisis de movimiento humano, ciencia de los datos.
Profesor de la Facultad de Ingeniería & Ciencias Naturales
Invest. Línea de Percep. Avanz. y Robótica – GITA
Director Grupo de Investigación MEDES.
Director del laboratorio de datos de la Uniautonoma.
pablo.caicedo.r@uniautonoma.edu.co
Lunes, Martes, Jueves y Viernes 11:00 – 13:00 Sala 504
Interpretes: Python, R, Latex(TEXLive), Anaconda.
IDE: Visual Studio Code, Google Colaboratory (R, Python)
Librerías Pandas, Matplotlib, Seaborn, Keras, Tensorflow, Numpy, SciKit-Learn, SciPy
Seguimiento de Aprendizaje: Moodle
B. Boehmke y B. M. Greenwell, Hands-on machine learning with R. Boca Raton: CRC Press, 2019.
G. Bonaccorso, Mastering machine learning algorithms: expert techniques to implement popular machine learning algorithms and fine-tune your models. 2018.
M. Fenner, Machine learning in python for everyone. Boston, MA: Addison-Wesley, 2019.
K. Kolodiazhnyi, Hands-On Machine Learning with C++ Build, Train, and Deploy End-To-end Machine Learning and Deep Learning Pipelines. Birmingham: Packt Publishing, Limited, 2020. Accedido: 28 de septiembre de 2021.
M. Kubat, An Introduction to Machine Learning. Cham: Springer International Publishing, 2017. doi: 10.1007/978-3-319-63913-0.
S. Raschka y V. Mirjalili, Python machine learning: machine learning and deep learning with Python, scikit-learn, and TensorFlow, Second edition, Fourth release,[fully revised and Updated]. Birmingham Mumbai: Packt Publishing, 04.
S. Skansi, Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence. Cham: Springer International Publishing, 2018. doi: 10.1007/978-3-319-73004-2.
The art of looking the underlying structure of the information through one or more datasets.
We look at numbers or graphs and try to find patterns. We pursue leads suggested by background information, imagination, patterns perceived, and experience with other data analyses.
EDA, depends on two things:
Type of variable scale (information type, categorical, numerical, continuous, discrete, etc).
Objective and type of the analysis (graphical, numerical, correlation, etc)
“Spotify offers digital copyright restricted recorded music and podcasts, including more than 82 million songs, from record labels and media companies” from wikipedia
The data set is located in the kaggle site. Dataset
Spotify wants to know if there is a relationship between the popularity of a song and the number of followers of its singers. The above to generate strategies to attract new singers to the platform.
Install conda environment manager
Install a suitable conda environment.
Install python libraries. At least, a machine learning project without deployment needs:
Install a suitable IDE software.
Script, script, script.
| id | name | popularity | duration_ms | explicit | artists | id_artists | release_date | danceability | energy | key | loudness | mode | speechiness | acousticness | instrumentalness | liveness | valence | tempo | time_signature | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 35iwgR4jXetI318WEWsa1Q | Carve | 6 | 126903 | 0 | ['Uli'] | ['45tIt06XoI0Iio4LBEVpls'] | 1922-02-22 | 0.645 | 0.4450 | 0 | -13.338 | 1 | 0.4510 | 0.674 | 0.7440 | 0.151 | 0.127 | 104.851 | 3 |
| 1 | 021ht4sdgPcrDgSk7JTbKY | Capítulo 2.16 - Banquero Anarquista | 0 | 98200 | 0 | ['Fernando Pessoa'] | ['14jtPCOoNZwquk5wd9DxrY'] | 1922-06-01 | 0.695 | 0.2630 | 0 | -22.136 | 1 | 0.9570 | 0.797 | 0.0000 | 0.148 | 0.655 | 102.009 | 1 |
| 2 | 07A5yehtSnoedViJAZkNnc | Vivo para Quererte - Remasterizado | 0 | 181640 | 0 | ['Ignacio Corsini'] | ['5LiOoJbxVSAMkBS2fUm3X2'] | 1922-03-21 | 0.434 | 0.1770 | 1 | -21.180 | 1 | 0.0512 | 0.994 | 0.0218 | 0.212 | 0.457 | 130.418 | 5 |
| 3 | 08FmqUhxtyLTn6pAh6bk45 | El Prisionero - Remasterizado | 0 | 176907 | 0 | ['Ignacio Corsini'] | ['5LiOoJbxVSAMkBS2fUm3X2'] | 1922-03-21 | 0.321 | 0.0946 | 7 | -27.961 | 1 | 0.0504 | 0.995 | 0.9180 | 0.104 | 0.397 | 169.980 | 3 |
| 4 | 08y9GfoqCWfOGsKdwojr5e | Lady of the Evening | 0 | 163080 | 0 | ['Dick Haymes'] | ['3BiJGZsyX9sJchTqcSA7Su'] | 1922 | 0.402 | 0.1580 | 3 | -16.900 | 0 | 0.0390 | 0.989 | 0.1300 | 0.311 | 0.196 | 103.220 | 4 |
| popularity | duration_ms | explicit | danceability | energy | key | loudness | mode | speechiness | acousticness | instrumentalness | liveness | valence | tempo | time_signature | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 586671.000000 | 5.866710e+05 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 | 586671.000000 |
| mean | 27.570100 | 2.300513e+05 | 0.044086 | 0.563594 | 0.542036 | 5.221603 | -10.206063 | 0.658797 | 0.104864 | 0.449862 | 0.113451 | 0.213935 | 0.552293 | 118.464856 | 3.873382 |
| std | 18.370623 | 1.265262e+05 | 0.205286 | 0.166102 | 0.251923 | 3.519426 | 5.089331 | 0.474114 | 0.179893 | 0.348836 | 0.266868 | 0.184326 | 0.257670 | 29.764133 | 0.473163 |
| min | 0.000000 | 3.344000e+03 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -60.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 13.000000 | 1.750930e+05 | 0.000000 | 0.453000 | 0.343000 | 2.000000 | -12.891000 | 0.000000 | 0.034000 | 0.096900 | 0.000000 | 0.098300 | 0.346000 | 95.600000 | 4.000000 |
| 50% | 27.000000 | 2.148930e+05 | 0.000000 | 0.577000 | 0.549000 | 5.000000 | -9.243000 | 1.000000 | 0.044300 | 0.422000 | 0.000024 | 0.139000 | 0.564000 | 117.384000 | 4.000000 |
| 75% | 41.000000 | 2.638670e+05 | 0.000000 | 0.686000 | 0.748000 | 8.000000 | -6.482000 | 1.000000 | 0.076300 | 0.785000 | 0.009550 | 0.278000 | 0.769000 | 136.321000 | 4.000000 |
| max | 100.000000 | 5.621218e+06 | 1.000000 | 0.991000 | 1.000000 | 11.000000 | 5.376000 | 1.000000 | 0.971000 | 0.996000 | 1.000000 | 1.000000 | 1.000000 | 246.381000 | 5.000000 |